Memory Bandwidth Explained: Why GB/s Matters More Than TFLOPS

When shopping for a GPU for local LLMs, most buyers focus on compute (TFLOPS) but the real bottleneck is memory bandwidth (GB/s). This guide explains why, with math and real benchmarks to back it up.

Why LLMs Are Memory-Bound

During token generation (autoregressive decoding), the model reads all its weights from VRAM for every single token. The computation involved is minimal — just a matrix-vector product. The time is spent waiting for weights to arrive from memory.

A simple formula: tokens/sec ≈ memory_bandwidth / model_size_bytes

Token Speed Calculator

GPUBW (GB/s)8B Q4 Speed32B Q4 Speed
RTX 3060 (12GB)360 GB/s~72 tok/sN/A (VRAM)
RTX 4070504 GB/s~100 tok/sN/A (VRAM)
RTX 40901,008 GB/s~122 tok/s~30 tok/s
Mac M4 Ultra800 GB/s~100 tok/s~22 tok/s
Tesla P40347 GB/s~42 tok/s~12 tok/s (24GB)

When TFLOPS Actually Matters

Compute (TFLOPS) matters during:

  • Prefill phase: Processing the input prompt (compute-bound)
  • Fine-tuning: Training involves large matrix multiplications
  • Large batch inference: vLLM with many concurrent requests

For practical token-speed benchmarks across different GPU generations, see LLMHardware.io's memory bandwidth benchmark page.

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